# Multiple parameters optimization having calculated and experimental values

2 vues (au cours des 30 derniers jours)
Eduardo Chacin le 7 Sep 2018
Commenté : Torsten le 11 Sep 2018
Hello everyone, I have a function that have to predict Hc and I have the Hcexperimental values. What I need to do, is optimize the 6 parameters that are in the function; so that the relative deviation between the calculated and experimental values becomes the smallest possible. I don't know if I need fmin, lsqnonlin, lsqcurvefit... I also don't know if I need multiple function files (.M files) to accomplish this. So far I've writen this:
function Hc = myfunction( P_k, T_k, c, z, w, v, IFexp )
y=T_k;
q=length(P_k);
%Initial values for parameters
par1=0.1442;
par2=2.6388;
par3=2.2083;
par4=0.2168;
par5=0.2;
par6=0.4;
%Ecuations
a=1.28+55.*(1./P_k+0.04).*exp(50.22./(T_k+230));
g=0.4+2084.69.*(1./P_k-0.002).*exp((-986.95)./(T_k+230));
x=(g./a).*c;
Hc=par1.*(x.^par2).*(y.^par3).*(z.^par4).*exp(par5.*w).*exp
(par6.*v);
disp(Hc)
RD=(IFexp-IFc)./IFexp.*100;
disp(RD)
ARD=100*(sum(RD))/q;
disp(ARD)
end
If someone could explain it to me detailed or show me an example with a script or even modify the script if needed; I'd be really grateful.
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### Réponse acceptée

Torsten le 7 Sep 2018
Modifié(e) : Torsten le 7 Sep 2018
function main
P_k = ...;
Hcexp = ...;
T_k = ...;
z = ...;
w = ...;
v = ...;
c = ...;
a = 1.28+55.*(1./P_k+0.04).*exp(50.22./(T_k+230));
g = 0.4+2084.69.*(1./P_k-0.002).*exp((-986.95)./(T_k+230));
x = (g./a).*c;
p0 = [0.1442;2.6388;2.2083;0.2168;0.2;0.4];
p = lsqnonlin(@(p)fun(p,x,T_k,z,w,v,Hcexp),p0)
end
function res = fun(p,x,y,z,w,v,Hcexp)
Hc = p(1).*x.^p(2).*y.^p(3).*z.^p(4).*exp(p(5).*w).*exp(p(6).*v);
res = (Hc-Hcexp)./Hcexp;
end
Best wishes
Torsten.
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Eduardo Chacin le 10 Sep 2018
@Torsten sorry, I did write "ub" instead of "up", but it has the same warning "Cannot solve problems with fewer equations than variables and with bounds. An error will be issued for this case in a future release. Ignoring bounds, using Levenberg-Marquardt method. "
Torsten le 11 Sep 2018
Then the warning says that it does not make sense to fit six parameters if you have less than six data points Hcexp.
And this warning is justified.
Best wishes
Torsten.

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